@article {4389, title = {The effect of environmental conditions on Atlantic salmon smolts{\textquoteright} (Salmo salar) bioenergetic requirements and migration through an inland sea}, journal = {Environmental Biology of Fishes}, volume = {101}, year = {2018}, month = {Jan-10-2018}, pages = {1467 - 1482}, issn = {0378-1909}, doi = {10.1007/s10641-018-0792-5}, url = {http://link.springer.com/10.1007/s10641-018-0792-5http://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdfhttp://link.springer.com/article/10.1007/s10641-018-0792-5/fulltext.htmlhttp://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdf}, author = {Strople, Leah C. and Filgueira, Ram{\'o}n and Hatcher, Bruce G. and Denny, Shelley and Bordeleau, Xavier and Whoriskey, Frederick G. and Crossin, Glenn T.} } @article {4399, title = {Modular and spatially explicit: A novel approach to system dynamics}, journal = {Environmental Modelling \& Software}, volume = {94}, year = {2017}, month = {Jan-08-2017}, pages = {48 - 62}, abstract = {

The Open Modeling Environment (OME) is an\ open-source System\ Dynamics (SD) simulation engine which has been created as a joint project between Oregon State University and the US\ Environmental Protection Agency. It is designed around a modular implementation, and provides a standardized interface for interacting with spatially explicit data while still supporting the standard SD model components. OME can be run as a standalone simulation or as a plugin to a larger simulation framework, and is capable of importing Models from several SD model formats, including Simile model files, Vensim model files, and the XMILE interchange format. While it has been released, OME is still under development, and a number of potential future improvements are discussed. To help illustrate the utility of OME, an example model design process is provided as an Appendix.

}, issn = {13648152}, doi = {10.1016/j.envsoft.2017.03.012}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1364815216308453https://api.elsevier.com/content/article/PII:S1364815216308453?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1364815216308453?httpAccept=text/plain}, author = {Wingo, Patrick and Brookes, Allen and Bolte, John} } @article {4262, title = {Multiscale digital Arabidopsis predicts individual organ and whole-organism growth}, journal = {Proceedings of the National Academy of Sciences}, year = {2014}, abstract = {Understanding how dynamic molecular networks affect whole-organism physiology, analogous to mapping genotype to phenotype, remains a key challenge in biology. Quantitative models that represent processes at multiple scales and link understanding from several research domains can help to tackle this problem. Such integrated models are more common in crop science and ecophysiology than in the research communities that elucidate molecular networks. Several laboratories have modeled particular aspects of growth in Arabidopsis thaliana, but it was unclear whether these existing models could productively be combined. We test this approach by constructing a multiscale model of Arabidopsis rosette growth. Four existing models were integrated with minimal parameter modification (leaf water content and one flowering parameter used measured data). The resulting framework model links genetic regulation and biochemical dynamics to events at the organ and whole-plant levels, helping to understand the combined effects of endogenous and environmental regulators on Arabidopsis growth. The framework model was validated and tested with metabolic, physiological, and biomass data from two laboratories, for five photoperiods, three accessions, and a transgenic line, highlighting the plasticity of plant growth strategies. The model was extended to include stochastic development. Model simulations gave insight into the developmental control of leaf production and provided a quantitative explanation for the pleiotropic developmental phenotype caused by overexpression of miR156, which was an open question. Modular, multiscale models, assembling knowledge from systems biology to ecophysiology, will help to understand and to engineer plant behavior from the genome to the field.}, issn = {1091-6490}, doi = {10.1073/pnas.1410238111}, url = {http://www.pnas.org/content/early/2014/08/27/1410238111.full.pdf+html?sid=66edb45d-8e99-4d84-a072-a47729a65e14}, author = {Chew, Y. H. and Wenden, B. and Flis, A. and Mengin, V. and Taylor, J. and Davey, C. L. and Tindal, C. and Thomas, H. and Ougham, H. J. and de Reffye, P. and Stitt, M. and Williams, M. and Muetzelfeldt, R. and Halliday, K. J. and Millar, A. J.} } @book {1594, title = {Environmental Modelling: Finding Simplicity in Complexity}, year = {2003}, pages = {430}, publisher = {Wiley}, organization = {Wiley}, keywords = {environmental modelling, GIS}, isbn = {978-0471496182}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471496189.html}, author = {John Wainwright and Mark Mulligan} }